Dengue is the most common arboviral infection worldwide and is found mainly in tropical and subtropical areas. The disease has been a growing threat for decades, and in 2019 the World Health Organization (WHO) ranked it among the top 10 threats to global health (World Health Organization, 2009; World Health Organization, 2020). It is estimated that 105 million people are infected by dengue annually, of whom half develop symptoms (Cattarino et al., 2020). Although most infections are asymptomatic or self-limiting, the small proportion of patients who develop complications can still overwhelm health systems in dengue-endemic countries during seasonal epidemics. Climate change, increasing global travel, and urbanization all serve to amplify the distribution of Aedes aegypti, the main vector responsible for dengue transmission (Whitehorn and Yacoub, 2019; Yacoub et al., 2011). Although dengue has been known for centuries, specific treatment is still to be found, and the two licensed vaccines are of limited benefit (Kariyawasam et al., 2023; Redoni et al., 2020).

Exploring the variation in viremia levels could help improve our understanding of disease pathogenesis and potentially facilitate assessment of new vaccines and treatments for dengue. For example, knowing the natural kinetics of dengue viremia may help in selection of patient groups or timing of antiviral use in therapeutic intervention trials. Dengue viremia kinetics has been investigated in several studies (Ben-Shachar et al., 2016; Clapham et al., 2016; Clapham et al., 2014; Duyen et al., 2011; Matangkasombut et al., 2020; Nguyet et al., 2013; Simmons et al., 2007a; Tricou et al., 2011). The main findings are: (1) viremia rapidly decreases after symptom onset; (2) dengue virus (DENV)-1 infection gives higher viremia level than DENV-2 and DENV-3; and (3) primary infection gives higher viremia than secondary infection in DENV-1. However, these studies had limited sample sizes, particularly for DENV-4 serotype, and did not provide a full account of viremia kinetics. Three studies constructed mechanistic mathematical models and focused on investigating the role of the host immune response in controlling viremia (Ben-Shachar et al., 2016; Clapham et al., 2016; Clapham et al., 2014). Another study assessed viremia in infants aged under 18 months (Simmons et al., 2007a); the findings cannot be generalized to older children or adults. Others focused on peak viremia level after symptom onset, time to viral clearance and/or the probability of detecting virus on each illness day (Duyen et al., 2011; Matangkasombut et al., 2020; Nguyet et al., 2013; Tricou et al., 2011). In most analyses, viremia levels below the limit of detection were simply set as zero, which may not be appropriate.

Quantifying the effect of viremia level on clinical outcomes is important to understand the mechanisms leading to severe disease. We and others have previously found that higher viremia levels are associated with more severe dengue (Duyen et al., 2011; Endy et al., 2004; Morsy et al., 2020; Tang et al., 2010; Tricou et al., 2011; Vaughn et al., 2000; Vuong et al., 2021), but the effect size was not strong (Vuong et al., 2021). However, all analyses were based on a single viremia value per individual. Investigating how individual viremia levels at different times after symptom onset affect the risk of severe outcome potentially provides new insights into pathogenic mechanisms. Another important question is whether a more rapid decline in viremia is associated with a lower risk of more severe clinical outcomes. One study has shown a slower rate of viral clearance being associated with more severe outcomes (Wang et al., 2006), while others have shown no association (Fox et al., 2011; Vaughn et al., 2000). This lack of conclusive evidence underscores the need for further investigation. In addition, the relationship between viremia kinetics and platelet count kinetics – an important hematological indicator – has yet to be investigated.

This study aims to improve our understanding of viremia kinetics, how it is associated with patient characteristics and virus serotype, and how it impacts disease severity, using a dataset derived from several thousand individuals with symptomatic dengue. First, we fitted a model for individual viremia kinetics that treats values below the detection limit as censored data, and assessed whether viremia kinetics differed by age, sex, serotype, or immune status. Second, we modeled platelet count kinetics and explored dependence on viremia level. We investigated the potential existence of a lagged effect of viremia, by analyzing platelet counts measured on and following the day of viremia assessment. Third, we modeled the effect of viremia levels measured at different days after symptom onset and the effect of the rate of viremia decline on development of two clinical outcomes, plasma leakage and severe dengue.

Baseline characteristics and outcomes are summarized in Table 1. Three quarters of the participants were children, and male sex predominated (60%). Most patients (85%) were enrolled on illness day 2 or 3. All four serotypes were included, but DENV-1 was the most prevalent (54%). Most infections (69%) were classified as probable secondary infection, while in 11% immune status could not be determined. There were 353 patients (15%) with plasma leakage and 65 patients (3%) with severe dengue. In Appendix 4—table 1, we summarize plasma leakage and severe dengue by serotype and immune status. DENV-2 had the highest proportion of these two outcomes. Patients with probable secondary infection were more likely to experience these outcomes than those with probable primary infection or indeterminate immune status, regardless of the infecting serotype.

Viremia kinetics and the relationship with clinical characteristics

Individual viremia trajectories are shown in Figure 2. Most individuals had a decreasing trend from day 1 or 2 onwards. However, values higher than the baseline value were observed up to day 7 (Appendix 4—figure 3). The decreasing trend was consistent across all combinations of serotype and immune status (Figure 2).

Figure 2

Download asset Open asset

In Figure 3, we present the fitted values based on the model for viremia for the one-step PCR cohort. The mean viremia trajectory differed by serotype. DENV-1 gave the highest viremia levels, with DENV-2 in secondary infection giving similar levels from day 5 onwards. DENV-2, DENV-3, and DENV-4 had similar viremia levels during the first 3 days. However, viremia decreased rapidly in DENV-4, reaching undetectable levels in the shortest time, while DENV-2 showed the slowest decline. These differences between serotypes were more pronounced in case of probable primary infection (Figure 3A). In terms of immune status, probable primary infection showed higher viremia levels compared to probable secondary infection in DENV-1 from day 3 onwards. The disparity between probable primary and probable secondary infection was less pronounced in the other serotypes (Figure 3B). Mean viremia levels were comparable between females and males (Figure 3C), as well as across age (Figure 3D). The one-step PCR method resulted in longer viremia compared to the two-step PCR (Appendix 5—figure 1). P values for the differences by age, serotype, immune status, and illness day were all <0.0001 (Appendix 5—table 1). The sensitivity analysis using the 10th-root transformation of viremia gave results similar to the main analysis using log-10 viremia (Appendix 5—figure 2).

Figure 3

Download asset Open asset

Effect of viremia on subsequent platelet count

Figure 4 shows the mean platelet counts by viremia levels based on the supermodel for the one-step PCR method. There was minimal impact of viremia on days 1–5 platelet count. However, higher viremia levels gave decreased subsequent platelet counts from day 6 onwards, for all serotypes. The strength of this effect increased with later illness day and was more pronounced in DENV-1 and DENV-2 compared to DENV-3 and DENV-4. All covariates had low p values (Appendix 6—table 1). The effect of viremia on subsequent platelet count remained consistent across subgroups of immune status, sex, and age. In the two-step PCR cohort, the effect of viremia on platelet count was less pronounced compared to the one-step PCR cohort, although the overall trends were similar (Appendix 6—figure 1).

Figure 4

Download asset Open asset

Effect of viremia on clinical outcomes

Higher viremia levels increased the risk of severe dengue and plasma leakage for each of the serotypes (Figure 5). The effect of viremia on both endpoints decreased with later landmark day of viremia measurement, particularly for severe dengue (Appendix 7—table 1). However, these trends were not clear in the results obtained from the simple logistic regression models at each landmark time point (Appendix 7—figure 1). This trend remained consistent across subgroups of sex, age, and study (Appendix 7—figure 2). Furthermore, the results were consistent between the analyses with and without imputation (Appendix 7—figure 3).

Figure 5

Download asset Open asset

The supermodel revealed that older individuals had a relatively lower risk of developing severe dengue, but the trend was not significant. Males exhibited a slightly higher risk of severe dengue compared to females. The effect of serotype on the two endpoints was dependent on immune status. Individuals with probable secondary infection had a higher risk of experiencing both endpoints compared to those with probable primary infection (Appendix 7—figure 4).

From the model of viremia kinetics with a linear trend of illness day, the individual rate of viremia decline ranged from 0.58 to 2.01 log-10 copies/ml/day, with a median of 1.39 log-10 copies/ml/day. The logistic regression model demonstrated that a faster decline in viremia reduced the risk of severe dengue and plasma leakage (Figure 6). The effect on plasma leakage was stronger in the probable secondary infection group (Appendix 7—table 2).

Figure 6

Download asset Open asset

In this study, we conducted a comprehensive analysis using a large dataset of 2340 viremic dengue patients to examine viremia kinetics from first presentation, its association with various virus and patient characteristics, and its impact on subsequent platelet count and disease severity. As others have described, we found that plasma viremia declines rapidly following the onset of symptoms, with the kinetics primarily influenced by the specific infecting serotype. Higher viremia levels were associated with a decrease in subsequent platelet count from day 6 onwards. Elevated viremia levels were found to increase the risk of developing severe dengue and plasma leakage, with the effect becoming weaker at later days. The effects of viremia on platelet count and clinical outcomes did not differ much by different subgroups of serotype, immune status, age, and sex. Moreover, a faster decline in viremia was associated with a reduced risk of the more severe clinical outcomes.

There is a limited number of published papers that studied the individual trajectory of dengue viremia. Most used data from Vietnam (Ben-Shachar et al., 2016; Clapham et al., 2016; Clapham et al., 2014; Duyen et al., 2011; Nguyet et al., 2013; Simmons et al., 2007a; Tricou et al., 2011), while one was from Thailand (Matangkasombut et al., 2020). Our study has provided further evidence supporting previous findings. We confirmed that (1) viremia rapidly decreases following the onset of symptoms, (2) DENV-1 exhibits higher viremia levels compared to DENV-2 and DENV-3, and (3) primary infection is associated with higher and more prolonged detectable viremia than secondary infection in DENV-1, while this pattern was not consistent across other serotypes. We have added viremia kinetics of DENV-4, and showed that viremia levels declined more rapidly than the other serotypes. Furthermore, our study demonstrated that the new PCR test has the ability to detect plasma viremia for a longer period compared to the older test. This may be attributed to the lower detection limits of the new test. Explaining the differences in viremia kinetics between serotypes remains challenging, as the molecular factors of the virus that influence plasma viremia levels are still unknown. It is also important to note that our study focused on the time since symptom onset rather than the time since infection. We cannot rule out that some of the observed differences are explained by a difference in time from infection to symptom onset.

Our study suggests that higher viremia levels on any day before day 6 are associated with reduced platelet count on days 6–8, typically corresponding to the nadir of platelet count. Additionally, it is possible that viremia in the initial 4 days after symptom onset could affect platelet count with a delay of 3–4 days. The direct involvement of the dengue virus in triggering platelet activation and apoptosis can contribute to the development of thrombocytopenia. Thrombocytopenia in dengue infection is thought to occur through two mechanisms: bone marrow suppression, which reduces thrombopoiesis, and increased peripheral platelet clearance (Quirino-Teixeira et al., 2022). Various processes contribute to these mechanisms, including platelet–leukocyte and platelet–endothelial cell interactions, phagocytosis, complement-mediated lysis, aggregation, and clot formation. Additionally, several host immune response factors are involved in platelet activation (Balakrishna Pillai et al., 2022). In our analysis, we considered interactions between viremia level and both serotype and immune status. Interestingly, we observed that the effect of viremia level on subsequent platelet count did not differ much by serotype and immune status.

Higher plasma viremia levels increase the risk of worse clinical outcomes, as demonstrated in our previous study that only utilized viremia at enrollment during the febrile phase (Vuong et al., 2021). The diminishing impact of viremia on the two endpoints on later illness days may be attributed to the heightened immune responses that are likely triggered by higher viremia levels, and the resulting complex interplay between these factors that underlies progression to severe dengue. The effects of viremia, age, serotype, immune status, and illness day on the clinical endpoints in this study are consistent with those observed in the previous study. The similarity between these two analyses, along with the weaker effect of viremia at later days, suggests that viremia levels around the time of symptom onset could serve as a reliable predictor of dengue severity. It suggests that the early febrile phase, specifically illness days 1–3, is the critical period of measuring viremia levels in clinical practice and dengue studies. Secondary infection remains a substantial risk factor for more severe outcomes, while viremia kinetics are not influenced much by immune status. These findings suggest that immune status and viremia may be independent predictors of clinical outcomes, following distinct pathways.

The landmark approach enabled us to investigate the effect of viremia on platelet count and the clinical endpoints at each illness day, ranging from days 1 to 7, while ensuring that the viremia measurements preceded the occurrence of the outcomes. The supermodel allows for investigation of trends of the effects, while gaining power because we assume the effects of some fixed factors (e.g., age and sex) to remain constant over time. There might be other potential confounders such as host genetic and immune response factors, but these data were not available. Moreover, we assumed that the relation between viremia level and platelet count was one-way: viremia level affects platelet count. Whether platelet count affects viremia is unknown.

A notable strength of our study is the utilization of a large pooled dataset, which allowed for a flexible modeling approach to investigate the influence of age, sex, serotype, and immune status, as well as their interactions and nonlinear trends. The use of a model that accounts for left-censored data was needed for capturing the distribution of viremia levels, considering the significant proportion of undetectable values observed.

The study has several limitations. First, the study only included patients from Vietnam, which may restrict the generalizability of the findings to other countries and regions. Second, our study assessed viral RNA in the plasma, including both viable and non-viable viral particles, which could potentially lead to an overestimation of the infectious viral particles in the blood. Lastly, data during the incubation period were unavailable as patients are typically not diagnosed with dengue until symptom onset. Therefore, the trajectory of viremia from the time of infection onwards could not be demonstrated, and symptom onset was used as the reference point instead of infection. This could potentially lead to an inaccurate interpretation of the results if the incubation period is influenced by the same factors that we included in our analyses. For example, in cases of secondary infection, the immune response is stronger and quicker than in primary infection. This might lead to a shorter duration between infection and symptom onset, as well as faster viral clearance.

In conclusion, our findings reveal that viremia levels exhibit a rapid decline shortly after symptom onset, becoming undetectable after approximately 1 week in the majority of patients. Viremia kinetics display variations depending on the infecting serotype. Higher viremia levels are associated with a subsequent reduction in platelet count on illness days 6–8, and an increased risk of experiencing more severe clinical outcomes. Viremia serves as an important predictor of dengue outcomes, independent of the host immune status. However, when considering our previous analysis that involved single early viremia measurements, the addition of daily viremia measurements may not enhance the prediction of worse clinical outcomes. Thus, the measurement of viremia levels during the early febrile phase is an important marker for clinical practice and dengue-related research. Moreover, a faster decline in viremia was found to be associated with a reduced risk of more severe clinical outcomes, suggesting that viremia kinetics could be a good surrogate endpoint for phase-2 dengue therapeutic trials.

All data generated or analyzed during this study and all codes have been deposited on GitHub at https://github.com/Nguyenlamvuong/Dengue_Viremia_Kinetics_eLife_2024 (copy archived at Vuong, 2024 and https://doi.org/10.5281/zenodo.11243209).

1. 1. Vuong NL

   (2024) Zenodo

   Nguyenlamvuong/Dengue_Viremia_Kinetics_eLife_2024: Dengue_Viremia_Kinetics_eLife_2024_with_data (dengue_viremia).

   https://doi.org/10.5281/zenodo.11243209

1. 1. Balakrishna Pillai AK
   2. Chu JJH
   3. Mariappan V
   4. JeanPierre AR

   (2022) Platelets in the pathogenesis of flavivirus disease

   Current Opinion in Virology 52:220–228.

   https://doi.org/10.1016/j.coviro.2021.12.007

   • PubMed
   • Google Scholar
2. 1. Bates D
   2. Maechler M
   3. Bolker B
   4. Walker S

   (2015) fitting linear mixed-effects models using lme4

   Journal of Statistical Software 67:1–48.

   https://doi.org/10.18637/jss.v067.i01

   • Google Scholar
3. 1. Ben-Shachar R
   2. Schmidler S
   3. Koelle K

   (2016) Drivers of inter-individual variation in dengue viral load dynamics

   PLOS Computational Biology 12:e1005194.

   https://doi.org/10.1371/journal.pcbi.1005194

   • PubMed
   • Google Scholar
4. 1. Cattarino L
   2. Rodriguez-Barraquer I
   3. Imai N
   4. Cummings DAT
   5. Ferguson NM

   (2020) Mapping global variation in dengue transmission intensity

   Science Translational Medicine 12:eaax4144.

   https://doi.org/10.1126/scitranslmed.aax4144

   • PubMed
   • Google Scholar
5. 1. Clapham HE
   2. Tricou V
   3. Van Vinh Chau N
   4. Simmons CP
   5. Ferguson NM

   (2014) Within-host viral dynamics of dengue serotype 1 infection

   Journal of the Royal Society, Interface 11:96.

   https://doi.org/10.1098/rsif.2014.0094

   • PubMed
   • Google Scholar
6. 1. Clapham HE
   2. Rodriguez-Barraquer I
   3. Azman AS
   4. Althouse BM
   5. Salje H
   6. Gibbons RV
   7. Rothman AL
   8. Jarman RG
   9. Nisalak A
   10. Thaisomboonsuk B
   11. Kalayanarooj S
   12. Nimmannitya S
   13. Vaughn DW
   14. Green S
   15. Yoon I-K
   16. Cummings DAT

   (2016) Dengue Virus (DENV) neutralizing antibody kinetics in children after symptomatic primary and postprimary DENV infection

   The Journal of Infectious Diseases 213:1428–1435.

   https://doi.org/10.1093/infdis/jiv759

   • PubMed
   • Google Scholar
7. 1. Duyen HTL
   2. Ngoc TV
   3. Ha DT
   4. Hang VTT
   5. Kieu NTT
   6. Young PR
   7. Farrar JJ
   8. Simmons CP
   9. Wolbers M
   10. Wills BA

   (2011) Kinetics of plasma viremia and soluble nonstructural protein 1 concentrations in dengue: differential effects according to serotype and immune status

   The Journal of Infectious Diseases 203:1292–1300.

   https://doi.org/10.1093/infdis/jir014

   • Google Scholar
8. 1. Endy TP
   2. Nisalak A
   3. Chunsuttitwat S
   4. Vaughn DW
   5. Green S
   6. Ennis FA
   7. Rothman AL
   8. Libraty DH

   (2004) Relationship of preexisting dengue virus (DV) neutralizing antibody levels to viremia and severity of disease in a prospective cohort study of DV infection in Thailand

   The Journal of Infectious Diseases 189:990–1000.

   https://doi.org/10.1086/382280

   • PubMed
   • Google Scholar
9. 1. Fox A
   2. Le NMH
   3. Simmons CP
   4. Wolbers M
   5. Wertheim HFL
   6. Pham TK
   7. Tran THN
   8. Trinh TML
   9. Nguyen TL
   10. Nguyen VT
   11. Nguyen DH
   12. Farrar J
   13. Horby P
   14. Taylor WR
   15. Nguyen VK

   (2011) Immunological and viral determinants of dengue severity in hospitalized adults in Ha Noi, Viet Nam

   PLOS Neglected Tropical Diseases 5:e967.

   https://doi.org/10.1371/journal.pntd.0000967

   • PubMed
   • Google Scholar
10. 1. Hadfield JD

    (2010) MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R Package

    Journal of Statistical Software 33:1–22.

    https://doi.org/10.18637/jss.v033.i02

    • Google Scholar
11. 1. Halekoh U
    2. Højsgaard S
    3. Yan J

    (2006) The R Package geepack for generalized estimating equations

    Journal of Statistical Software 01:1–11.

    https://doi.org/10.18637/jss.v015.i02

    • Google Scholar
12. Software

    1. Harrell FE

    (2021) Rms: regression modeling strategies, version 6.2-0

    R Package.

    https://CRAN.R-project.org/package=rms
13. Software

    1. Hartig F

    (2021) Dharma: residual diagnostics for Hierarchical (multi-level / mixed) regression models, version version 0.4.3

    R Package.

    https://CRAN.R-project.org/package=DHARMa
14. 1. Hue KDT
    2. Tuan TV
    3. Thi HTN
    4. Bich CTN
    5. Anh HHL
    6. Wills BA
    7. Simmons CP

    (2011) Validation of an internally controlled one-step real-time multiplex RT-PCR assay for the detection and quantitation of dengue virus RNA in plasma

    Journal of Virological Methods 177:168–173.

    https://doi.org/10.1016/j.jviromet.2011.08.002

    • PubMed
    • Google Scholar
15. 1. Jaenisch T
    2. Tam DTH
    3. Kieu NTT
    4. Van Ngoc T
    5. Nam NT
    6. Van Kinh N
    7. Yacoub S
    8. Chanpheaktra N
    9. Kumar V
    10. See LLC
    11. Sathar J
    12. Sandoval EP
    13. Alfaro GMM
    14. Laksono IS
    15. Mahendradhata Y
    16. Sarker M
    17. Ahmed F
    18. Caprara A
    19. Benevides BS
    20. Marques ETA
    21. Magalhaes T
    22. Brasil P
    23. Netto M
    24. Tami A
    25. Bethencourt SE
    26. Guzman M
    27. Simmons C
    28. Quyen NTH
    29. Merson L
    30. Dung NTP
    31. Beck D
    32. Wirths M
    33. Wolbers M
    34. Lam PK
    35. Rosenberger K
    36. Wills B

    (2016) Clinical evaluation of dengue and identification of risk factors for severe disease: protocol for a multicentre study in 8 countries

    BMC Infectious Diseases 16:120.

    https://doi.org/10.1186/s12879-016-1440-3

    • PubMed
    • Google Scholar
16. 1. Kariyawasam R
    2. Lachman M
    3. Mansuri S
    4. Chakrabarti S
    5. Boggild AK

    (2023) A dengue vaccine whirlwind update

    Therapeutic Advances in Infectious Disease 10:20499361231167274.

    https://doi.org/10.1177/20499361231167274

    • PubMed
    • Google Scholar
17. 1. Matangkasombut P
    2. Manopwisedjaroen K
    3. Pitabut N
    4. Thaloengsok S
    5. Suraamornkul S
    6. Yingtaweesak T
    7. Duong V
    8. Sakuntabhai A
    9. Paul R
    10. Singhasivanon P

    (2020) Dengue viremia kinetics in asymptomatic and symptomatic infection

    International Journal of Infectious Diseases 101:90–97.

    https://doi.org/10.1016/j.ijid.2020.09.1446

    • PubMed
    • Google Scholar
18. 1. Morsy S
    2. Hashan MR
    3. Hieu TH
    4. Mohammed AT
    5. Elawady SS
    6. Ghosh P
    7. Elgendy MA
    8. Le H-H
    9. Hamad WMA
    10. Iqtadar S
    11. Dumre SP
    12. Hirayama K
    13. Huy NT

    (2020) The association between dengue viremia kinetics and dengue severity: a systemic review and meta-analysis

    Reviews in Medical Virology 30:1–10.

    https://doi.org/10.1002/rmv.2121

    • PubMed
    • Google Scholar
19. 1. Nguyet MN
    2. Duong THK
    3. Trung VT
    4. Nguyen THQ
    5. Tran CNB
    6. Long VT
    7. Dui LT
    8. Nguyen HL
    9. Farrar JJ
    10. Holmes EC
    11. Rabaa MA
    12. Bryant JE
    13. Nguyen TT
    14. Nguyen HTC
    15. Nguyen LTH
    16. Pham MP
    17. Nguyen HT
    18. Luong TTH
    19. Wills B
    20. Nguyen CVV
    21. Wolbers M
    22. Simmons CP

    (2013) Host and viral features of human dengue cases shape the population of infected and infectious Aedes aegypti mosquitoes

    PNAS 110:9072–9077.

    https://doi.org/10.1073/pnas.1303395110

    • PubMed
    • Google Scholar
20. 1. Quirino-Teixeira AC
    2. Andrade FB
    3. Pinheiro MBM
    4. Rozini SV
    5. Hottz ED

    (2022) Platelets in dengue infection: more than a numbers game

    Platelets 33:176–183.

    https://doi.org/10.1080/09537104.2021.1921722

    • PubMed
    • Google Scholar
21. Software

    1. R Core Team

    (2021) A language and environment for statistical computing, version 4.4.1

    R Foundation for Statistical Computing.

    https://www.R-project.org/
22. 1. Redoni M
    2. Yacoub S
    3. Rivino L
    4. Giacobbe DR
    5. Luzzati R
    6. Di Bella S

    (2020) Dengue: status of current and under-development vaccines

    Reviews in Medical Virology 30:e2101.

    https://doi.org/10.1002/rmv.2101

    • PubMed
    • Google Scholar
23. 1. Simmons CP
    2. Chau TNB
    3. Thuy TT
    4. Tuan NM
    5. Hoang DM
    6. Thien NT
    7. Lien LB
    8. Quy NT
    9. Hieu NT
    10. Hien TT
    11. McElnea C
    12. Young P
    13. Whitehead S
    14. Hung NT
    15. Farrar J

    (2007a) Maternal antibody and viral factors in the pathogenesis of dengue virus in infants

    The Journal of Infectious Diseases 196:416–424.

    https://doi.org/10.1086/519170

    • PubMed
    • Google Scholar
24. 1. Simmons CP
    2. Popper S
    3. Dolocek C
    4. Chau TNB
    5. Griffiths M
    6. Dung NTP
    7. Long TH
    8. Hoang DM
    9. Chau NV
    10. Thao LTT
    11. Hien TT
    12. Relman DA
    13. Farrar J

    (2007b) Patterns of host genome-wide gene transcript abundance in the peripheral blood of patients with acute dengue hemorrhagic fever

    The Journal of Infectious Diseases 195:1097–1107.

    https://doi.org/10.1086/512162

    • PubMed
    • Google Scholar
25. 1. Tang Y
    2. Kou Z
    3. Zhang F
    4. Yao X
    5. Liu S
    6. Ma J
    7. Zhou Y
    8. Zhao W
    9. Tang X
    10. Jin X

    (2010) Both viremia and cytokine levels associate with the lack of severe disease in secondary dengue 1 infection among adult Chinese patients

    PLOS ONE 5:e15631.

    https://doi.org/10.1371/journal.pone.0015631

    • PubMed
    • Google Scholar
26. 1. Tomashek KM
    2. Wills B
    3. See Lum LC
    4. Thomas L
    5. Durbin A
    6. Leo Y-S
    7. de Bosch N
    8. Rojas E
    9. Hendrickx K
    10. Erpicum M
    11. Agulto L
    12. Jaenisch T
    13. Tissera H
    14. Suntarattiwong P
    15. Collers BA
    16. Wallace D
    17. Schmidt AC
    18. Precioso A
    19. Narvaez F
    20. Thomas SJ
    21. Edelman R
    22. Siqueira JB
    23. Cassetti MC
    24. Dempsey W
    25. Gubler DJ

    (2018) Development of standard clinical endpoints for use in dengue interventional trials

    PLOS Neglected Tropical Diseases 12:e0006497.

    https://doi.org/10.1371/journal.pntd.0006497

    • PubMed
    • Google Scholar
27. 1. Tricou V
    2. Minh NN
    3. Farrar J
    4. Tran HT
    5. Simmons CP

    (2011) Kinetics of viremia and NS1 antigenemia are shaped by immune status and virus serotype in adults with dengue

    PLOS Neglected Tropical Diseases 5:e1309.

    https://doi.org/10.1371/journal.pntd.0001309

    • PubMed
    • Google Scholar
28. Book

    1. van Houwelingen H
    2. Putter H

    (2011) Dynamic Prediction in Clinical Survival Analysis

    CRC Press.

    https://doi.org/10.1201/b11311

    • Google Scholar
29. 1. Vaughn DW
    2. Green S
    3. Kalayanarooj S
    4. Innis BL
    5. Nimmannitya S
    6. Suntayakorn S
    7. Endy TP
    8. Raengsakulrach B
    9. Rothman AL
    10. Ennis FA
    11. Nisalak A

    (2000) Dengue viremia titer, antibody response pattern, and virus serotype correlate with disease severity

    The Journal of Infectious Diseases 181:2–9.

    https://doi.org/10.1086/315215

    • PubMed
    • Google Scholar
30. 1. Vuong NL
    2. Quyen NTH
    3. Tien NTH
    4. Tuan NM
    5. Kien DTH
    6. Lam PK
    7. Tam DTH
    8. Van Ngoc T
    9. Yacoub S
    10. Jaenisch T
    11. Geskus RB
    12. Simmons CP
    13. Wills BA

    (2021) Higher plasma viremia in the febrile phase is associated with adverse dengue outcomes irrespective of infecting serotype or host immune status: an analysis of 5642 vietnamese cases

    Clinical Infectious Diseases 72:e1074–e1083.

    https://doi.org/10.1093/cid/ciaa1840

    • PubMed
    • Google Scholar
31. Software

    1. Vuong NL

    (2024) Dengue_Viremia_Kinetics_Elife_2024, version swh:1:rev:07701a4cba5cb88b15d3def4ed08ece651e7bcd6

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:24c646cecf398bfa23c0208c74fadf22a3e20d43;origin=https://github.com/Nguyenlamvuong/Dengue_Viremia_Kinetics_eLife_2024;visit=swh:1:snp:b00f03cd1d1f7e01b05b25574526336a9eaa29e4;anchor=swh:1:rev:07701a4cba5cb88b15d3def4ed08ece651e7bcd6
32. 1. Wang W-K
    2. Chen H-L
    3. Yang C-F
    4. Hsieh S-C
    5. Juan C-C
    6. Chang S-M
    7. Yu C-C
    8. Lin L-H
    9. Huang J-H
    10. King C-C

    (2006) Slower rates of clearance of viral load and virus-containing immune complexes in patients with dengue hemorrhagic fever

    Clinical Infectious Diseases 43:1023–1030.

    https://doi.org/10.1086/507635

    • PubMed
    • Google Scholar
33. 1. Whitehorn J
    2. Yacoub S

    (2019) Global warming and arboviral infections

    Clinical Medicine 19:149–152.

    https://doi.org/10.7861/clinmedicine.19-2-149

    • PubMed
    • Google Scholar
34. Book

    1. World Health Organization

    (2009)

    Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control: World Health Organization

    World Health Organization.

    • Google Scholar
35. Website

    1. World Health Organization

    (2020) Ten threats to global health in 2019

    Accessed May 22, 2024.

    https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-2019
36. 1. Yacoub S
    2. Kotit S
    3. Yacoub MH

    (2011) Disease appearance and evolution against a background of climate change and reduced resources

    Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences 369:1719–1729.

    https://doi.org/10.1098/rsta.2011.0013

    • PubMed
    • Google Scholar

Author details

1. Nguyen Lam Vuong

   1. University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Viet Nam
   2. Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   vuongnl@oucru.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2684-3041

2. Nguyen Than Ha Quyen

   Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam

   Contribution

   Resources, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

3. Nguyen Thi Hanh Tien

   Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam

   Contribution

   Resources, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

4. Kien Duong Thi Hue

   Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam

   Contribution

   Resources, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Huynh Thi Le Duyen

   Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam

   Contribution

   Resources, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

6. Phung Khanh Lam

   1. University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Viet Nam
   2. Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam

   Contribution

   Resources, Data curation, Supervision, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7968-473X

7. Dong Thi Hoai Tam

   Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam

   Contribution

   Conceptualization, Resources, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Tran Van Ngoc

   Hospital for Tropical Diseases, Ho Chi Minh City, Viet Nam

   Contribution

   Conceptualization, Resources, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

9. Thomas Jaenisch

   1. Center for Global Health, Colorado School of Public Health, Aurora, United States
   2. Heidelberg Institute of Global Health (HIGH), Heidelberg University Hospital, Heidelberg, Germany

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Writing – review and editing

   Competing interests

   reports receiving personal fees as members of the Roche Pharmaceuticals Advisory Board on Severe Dengue, outside the submitted work

10. Cameron P Simmons

    1. Centre for Tropical Medicine and Global health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
    2. World Mosquito Program, Monash University, Clayton, Australia

    Contribution

    Conceptualization, Funding acquisition, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9039-7392

11. Sophie Yacoub

    1. Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    2. Centre for Tropical Medicine and Global health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom

    Contribution

    Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing – review and editing

    Competing interests

    reports receiving personal honorarium for attending the Novartis dengue drug ad board meeting and Takeda dengue education symposium, outside the submitted work

12. Bridget A Wills

    1. Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    2. Centre for Tropical Medicine and Global health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom

    Contribution

    Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing – review and editing

    Competing interests

    reports receiving personal fees (a) as a member of the Roche Advisory Board on Severe Dengue and (b) as a member of the Data Monitoring and Adjudication Committees for the Takeda dengue vaccine trials, both outside the remit of the submitted work

    "This ORCID iD identifies the author of this article:" 0000-0001-9086-8804

13. Ronald Geskus

    1. Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    2. Centre for Tropical Medicine and Global health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom

    Contribution

    Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing – review and editing

    For correspondence

    rgeskus@oucru.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2740-3155

• 548

  views

• 71

  downloads

• 1

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.